NCAP: Scene Text Image Super-Resolution with Non-CAtegorical Prior

• URL: http://arxiv.org/abs/2504.00410v1
• Date: Tue, 01 Apr 2025 04:14:07 GMT
• Title: NCAP: Scene Text Image Super-Resolution with Non-CAtegorical Prior
• Authors: Dongwoo Park, Suk Pil Ko,
• Abstract summary: Scene text image super-resolution (STISR) enhances the resolution and quality of low-resolution images.<n>Explicit categorical priors, like TP, can negatively impact STISR if incorrect.<n>Pre-trained recognizers used to generate TP struggle with low-resolution images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scene text image super-resolution (STISR) enhances the resolution and quality of low-resolution images. Unlike previous studies that treated scene text images as natural images, recent methods using a text prior (TP), extracted from a pre-trained text recognizer, have shown strong performance. However, two major issues emerge: (1) Explicit categorical priors, like TP, can negatively impact STISR if incorrect. We reveal that these explicit priors are unstable and propose replacing them with Non-CAtegorical Prior (NCAP) using penultimate layer representations. (2) Pre-trained recognizers used to generate TP struggle with low-resolution images. To address this, most studies jointly train the recognizer with the STISR network to bridge the domain gap between low- and high-resolution images, but this can cause an overconfidence phenomenon in the prior modality. We highlight this issue and propose a method to mitigate it by mixing hard and soft labels. Experiments on the TextZoom dataset demonstrate an improvement by 3.5%, while our method significantly enhances generalization performance by 14.8\% across four text recognition datasets. Our method generalizes to all TP-guided STISR networks.

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